Looking for ozone recovery in the Arctic
Bibliographic record
Abstract
Polar regions are strategic in the study of stratospheric long-term ozone trends: since these regions are highly impacted by the effective-chlorine levels, the ozone recovery expected from the reduced emission of ozone depleting substances (Montreal Protocol) should be observed most easily there. However, contrary to the Antarctic, positive ozone trends have not yet been observed in the Arctic (WMO 2022) due to the higher natural variability of ozone in that region. Studying tropospheric ozone trends in the Arctic is also crucial because it can help in reconciling total and stratospheric ozone trends, additionally to the intrinsic interest in ground-level ozone as one of the main greenhouse gases.The Network for the Detection of Atmospheric Composition Change (NDACC) provides amongst others long-term ozone data from Fourier Transform Infrared (FTIR) spectrometers as well as ozone sonde instruments. We present long-term trends (2000-2022) for total, stratospheric and tropospheric ozone from seven FTIR ground-based stations and from seven ozone sonde stations in the Arctic. The FTIR stratospheric trends are provided in three different layers, covering the lower stratosphere up to 45 km, according to the FTIR vertical resolution. Based on a previous representativeness study, we also obtain regional trends with reduced uncertainties by combining different instruments and stations. Annual and seasonal trends are calculated using a multiple linear regression technique involving a set of proxies that represent physical processes influencing the natural ozone variability. Using this network of ground-based measurements, we further validate tropospheric and stratospheric ozone trends in the Arctic as derived from satellite observations (MEGRIDOP, SUNLIT, IASI).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".